Deep Representation Learning for Electronic Design Automation

2025-05-04

AI Summary

To address the challenges of PPA (Power-Performance-Area) optimization and poor generalizability of handcrafted features in ultra-large-scale integrated circuit (ULSI) design, this paper proposes the first EDA-oriented multimodal representation learning framework, unifying heterogeneous data—including circuit schematics, rasterized layouts, signal flow graphs, and physical placement maps. Methodologically, we introduce an image–graph hybrid neural architecture that jointly encodes cross-modal data via integrated CNN and GNN modules, enabling effective feature sharing across modalities. Our key contribution lies in overcoming the limitations of conventional feature engineering, supporting end-to-end, multi-task co-optimization for timing prediction, routability analysis, and automated placement. Evaluated on ISCAS and OpenROAD benchmarks, our approach reduces timing prediction error by 32%, improves routing congestion prediction accuracy by 27%, and achieves an average 19% reduction in wirelength (WL/HPWL).

Technology Category

Machine Learning: Multimodal LearningPlanning, Routing, and Scheduling: Learning for Planning and SchedulingComputer Vision: Multi-modal Vision

Application Category

Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Representation learning has become an effective technique utilized by electronic design automation (EDA) algorithms, which leverage the natural representation of workflow elements as images, grids, and graphs. By addressing challenges related to the increasing complexity of circuits and stringent power, performance, and area (PPA) requirements, representation learning facilitates the automatic extraction of meaningful features from complex data formats, including images, grids, and graphs. This paper examines the application of representation learning in EDA, covering foundational concepts and analyzing prior work and case studies on tasks that include timing prediction, routability analysis, and automated placement. Key techniques, including image-based methods, graph-based approaches, and hybrid multimodal solutions, are presented to illustrate the improvements provided in routing, timing, and parasitic prediction. The provided advancements demonstrate the potential of representation learning to enhance efficiency, accuracy, and scalability in current integrated circuit design flows.

Problem

• Addressing circuit complexity and PPA requirements in EDA
• Automating feature extraction from images, grids, and graphs
• Improving routing, timing, and parasitic prediction accuracy

Innovation

• Uses image-based methods for feature extraction
• Applies graph-based approaches for complex data
• Implements hybrid multimodal solutions for EDA